Joint POCS method with compressive sensing theory for super-resolution image reconstruction

In this paper, we propose to improve the traditional projection onto convex sets (POCS) super-resolution reconstruction (SRR) method by combining a newly-developed compressive sensing (CS) theory. This compressive sensing theory is more recently adapted to super-resolution reconstruction. The only requirement is that the image is known to be sparse, which is a specific but very general and wide-spread property of natural signal. Experimental results exhibit visible improvement on reconstructed image towards traditional POCS method.